Interpretation of convolutional neural networks reveals crucial sequence features involving in transcription during fiber development.

ABSTRACT:

Background

Upland cotton provides the most natural fiber in the world. During fiber development, the quality and yield of fiber were influenced by gene transcription. Revealing sequence features related to transcription has a profound impact on cotton molecular breeding. We applied convolutional neural networks to predict gene expression status based on the sequences of gene transcription start regions. After that, a gradient-based interpretation and an N-adjusted kernel transformation were implemented to extract sequence features contributing to transcription.

Results

Our models had approximate 80% accuracies, and the area under the receiver operating characteristic curve reached over 0.85. Gradient-based interpretation revealed 5' untranslated region contributed to gene tr

SUBMITTER: Liu S 

PROVIDER: S-EPMC8922751 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature